Overview

Dataset statistics

Number of variables14
Number of observations6268
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory734.5 KiB
Average record size in memory120.0 B

Variable types

NUM9
CAT5

Warnings

currency has constant value "6268" Constant
track_name has a high cardinality: 6266 distinct values High cardinality
version_num has a high cardinality: 1532 distinct values High cardinality
size_Megabytes is highly correlated with size_bytesHigh correlation
size_bytes is highly correlated with size_MegabytesHigh correlation
price is highly skewed (γ1 = 30.94413896) Skewed
track_name is uniformly distributed Uniform
id has unique values Unique
price has 3383 (54.0%) zeros Zeros
screenshot_num has 1006 (16.0%) zeros Zeros

Reproduction

Analysis started2020-11-16 05:28:12.269949
Analysis finished2020-11-16 05:28:32.883055
Duration20.61 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

id
Real number (ℝ≥0)

UNIQUE

Distinct6268
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean841755890.1
Minimum281656475
Maximum1188375727
Zeros0
Zeros (%)0.0%
Memory size49.0 KiB
2020-11-15T21:28:33.051527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum281656475
5-th percentile352565640.3
Q1575110173
median953938352.5
Q31075350065
95-th percentile1146662950
Maximum1188375727
Range906719252
Interquartile range (IQR)500239892.2

Descriptive statistics

Standard deviation275748438.4
Coefficient of variation (CV)0.3275871801
Kurtosis-1.150051562
Mean841755890.1
Median Absolute Deviation (MAD)161163838.5
Skewness-0.5940435087
Sum5.276125919e+12
Variance7.603720127e+16
MonotocityNot monotonic
2020-11-15T21:28:33.218812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
10249538241< 0.1%
 
5012200131< 0.1%
 
5753575891< 0.1%
 
11459201501< 0.1%
 
10171480551< 0.1%
 
11651099121< 0.1%
 
10975647431< 0.1%
 
8923202941< 0.1%
 
3832982041< 0.1%
 
4939127331< 0.1%
 
Other values (6258)625899.8%
 
ValueCountFrequency (%) 
2816564751< 0.1%
 
2817961081< 0.1%
 
2819402921< 0.1%
 
2826142161< 0.1%
 
2829357061< 0.1%
 
ValueCountFrequency (%) 
11883757271< 0.1%
 
11878387701< 0.1%
 
11877795321< 0.1%
 
11876823901< 0.1%
 
11876174751< 0.1%
 

track_name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct6266
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size49.0 KiB
Mannequin Challenge
 
2
VR Roller Coaster
 
2
Moodnotes - Thought Journal / Mood Diary
 
1
Ninja Kid Run VR: Runner & Racing Games For Free
 
1
BFB Champions 2.0 ~Football Club Manager~
 
1
Other values (6261)
6261 
ValueCountFrequency (%) 
Mannequin Challenge2< 0.1%
 
VR Roller Coaster2< 0.1%
 
Moodnotes - Thought Journal / Mood Diary1< 0.1%
 
Ninja Kid Run VR: Runner & Racing Games For Free1< 0.1%
 
BFB Champions 2.0 ~Football Club Manager~1< 0.1%
 
The Fourth Dimension1< 0.1%
 
Hammer Time!1< 0.1%
 
Cartwheel by Target1< 0.1%
 
T-Mobile Tuesdays1< 0.1%
 
Shimmer and Shine: Enchanted Carpet Ride Game HD1< 0.1%
 
Other values (6256)625699.8%
 
2020-11-15T21:28:33.420090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique6264 ?
Unique (%)99.9%
2020-11-15T21:28:33.594750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length232
Median length21
Mean length26.18363114
Min length2

size_bytes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6205
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205743011.8
Minimum589824
Maximum4025969664
Zeros0
Zeros (%)0.0%
Memory size49.0 KiB
2020-11-15T21:28:33.755117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum589824
5-th percentile13055488
Q151719424
median102129152
Q3188477440
95-th percentile877646284.8
Maximum4025969664
Range4025379840
Interquartile range (IQR)136758016

Descriptive statistics

Standard deviation352634148.9
Coefficient of variation (CV)1.713954442
Kurtosis27.92901488
Mean205743011.8
Median Absolute Deviation (MAD)61154816
Skewness4.583617734
Sum1.289597198e+12
Variance1.24350843e+17
MonotocityNot monotonic
2020-11-15T21:28:33.921085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1017354242< 0.1%
 
378286082< 0.1%
 
275619842< 0.1%
 
1578711042< 0.1%
 
1032335362< 0.1%
 
1436794882< 0.1%
 
511795202< 0.1%
 
361840642< 0.1%
 
483594242< 0.1%
 
358072322< 0.1%
 
Other values (6195)624899.7%
 
ValueCountFrequency (%) 
5898241< 0.1%
 
6184961< 0.1%
 
6717441< 0.1%
 
6988001< 0.1%
 
7096321< 0.1%
 
ValueCountFrequency (%) 
40259696641< 0.1%
 
38961090561< 0.1%
 
38604062721< 0.1%
 
38565888001< 0.1%
 
36469934081< 0.1%
 

currency
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.0 KiB
USD
6268 
ValueCountFrequency (%) 
USD6268100.0%
 
2020-11-15T21:28:34.093453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-15T21:28:34.174931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:34.252908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

price
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct35
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.821976707
Minimum0
Maximum299.99
Zeros3383
Zeros (%)54.0%
Memory size49.0 KiB
2020-11-15T21:28:34.375571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.99
95-th percentile6.99
Maximum299.99
Range299.99
Interquartile range (IQR)2.99

Descriptive statistics

Standard deviation6.128237869
Coefficient of variation (CV)3.363510546
Kurtosis1342.302023
Mean1.821976707
Median Absolute Deviation (MAD)0
Skewness30.94413896
Sum11420.15
Variance37.55529938
MonotocityNot monotonic
2020-11-15T21:28:34.519308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%) 
0338354.0%
 
2.9965010.4%
 
0.9963110.1%
 
1.995879.4%
 
4.993695.9%
 
3.992564.1%
 
6.991622.6%
 
9.99731.2%
 
5.99410.7%
 
7.99280.4%
 
Other values (25)881.4%
 
ValueCountFrequency (%) 
0338354.0%
 
0.9963110.1%
 
1.995879.4%
 
2.9965010.4%
 
3.992564.1%
 
ValueCountFrequency (%) 
299.991< 0.1%
 
249.991< 0.1%
 
99.991< 0.1%
 
74.991< 0.1%
 
59.993< 0.1%
 

rating_count_tot
Real number (ℝ≥0)

Distinct3184
Distinct (%)50.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14803.80552
Minimum1
Maximum2974676
Zeros0
Zeros (%)0.0%
Memory size49.0 KiB
2020-11-15T21:28:34.678260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q178
median512.5
Q33963.5
95-th percentile57446.8
Maximum2974676
Range2974675
Interquartile range (IQR)3885.5

Descriptive statistics

Standard deviation80984.68437
Coefficient of variation (CV)5.470531497
Kurtosis496.3360944
Mean14803.80552
Median Absolute Deviation (MAD)499
Skewness18.33563392
Sum92790253
Variance6558519103
MonotocityNot monotonic
2020-11-15T21:28:34.839471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
11201.9%
 
7480.8%
 
9470.7%
 
5460.7%
 
6440.7%
 
10370.6%
 
14370.6%
 
2350.6%
 
12350.6%
 
3330.5%
 
Other values (3174)578692.3%
 
ValueCountFrequency (%) 
11201.9%
 
2350.6%
 
3330.5%
 
480.1%
 
5460.7%
 
ValueCountFrequency (%) 
29746761< 0.1%
 
21615581< 0.1%
 
21308051< 0.1%
 
17245461< 0.1%
 
11268791< 0.1%
 

user_rating
Real number (ℝ≥0)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.049696873
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size49.0 KiB
2020-11-15T21:28:34.981589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.5
Q14
median4.5
Q34.5
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.7269427319
Coefficient of variation (CV)0.1795054679
Kurtosis2.840438346
Mean4.049696873
Median Absolute Deviation (MAD)0.5
Skewness-1.54273501
Sum25383.5
Variance0.5284457354
MonotocityDecreasing
2020-11-15T21:28:35.098917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
4.5266342.5%
 
4162625.9%
 
3.570211.2%
 
54927.8%
 
33836.1%
 
2.51963.1%
 
21061.7%
 
1.5560.9%
 
1440.7%
 
ValueCountFrequency (%) 
1440.7%
 
1.5560.9%
 
21061.7%
 
2.51963.1%
 
33836.1%
 
ValueCountFrequency (%) 
54927.8%
 
4.5266342.5%
 
4162625.9%
 
3.570211.2%
 
33836.1%
 

version_num
Categorical

HIGH CARDINALITY

Distinct1532
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Memory size49.0 KiB
1.0
 
269
1.1
 
234
1.2
 
195
1.0.0
 
136
1.3
 
120
Other values (1527)
5314 
ValueCountFrequency (%) 
1.02694.3%
 
1.12343.7%
 
1.21953.1%
 
1.0.01362.2%
 
1.31201.9%
 
1.0.11081.7%
 
1.4971.5%
 
1.0.2921.5%
 
2.0881.4%
 
1.5821.3%
 
Other values (1522)484777.3%
 
2020-11-15T21:28:35.270222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique940 ?
Unique (%)15.0%
2020-11-15T21:28:35.429090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length5
Mean length4.488991704
Min length1

cont_rating
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size49.0 KiB
4+
3937 
12+
1004 
9+
886 
17+
441 
ValueCountFrequency (%) 
4+393762.8%
 
12+100416.0%
 
9+88614.1%
 
17+4417.0%
 
2020-11-15T21:28:35.569659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-15T21:28:35.665991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:35.771607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.230536056
Min length2

prime_genre
Categorical

Distinct23
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size49.0 KiB
Games
3400 
Entertainment
471 
Education
387 
Photo & Video
 
325
Utilities
 
219
Other values (18)
1466 
ValueCountFrequency (%) 
Games340054.2%
 
Entertainment4717.5%
 
Education3876.2%
 
Photo & Video3255.2%
 
Utilities2193.5%
 
Productivity1722.7%
 
Health & Fitness1592.5%
 
Social Networking1342.1%
 
Music1342.1%
 
Lifestyle1131.8%
 
Other values (13)75412.0%
 
2020-11-15T21:28:35.916285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-15T21:28:36.072068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length17
Median length5
Mean length7.455169113
Min length4

sup_devices_num
Real number (ℝ≥0)

Distinct20
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.25941289
Minimum9
Maximum47
Zeros0
Zeros (%)0.0%
Memory size49.0 KiB
2020-11-15T21:28:36.199118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile25
Q137
median37
Q338
95-th percentile43
Maximum47
Range38
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.907911453
Coefficient of variation (CV)0.1048838709
Kurtosis9.297560356
Mean37.25941289
Median Absolute Deviation (MAD)1
Skewness-2.595233323
Sum233542
Variance15.27177192
MonotocityNot monotonic
2020-11-15T21:28:36.320240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%) 
37290046.3%
 
38161625.8%
 
4092014.7%
 
433415.4%
 
242644.2%
 
25631.0%
 
26390.6%
 
39390.6%
 
47260.4%
 
35210.3%
 
Other values (10)390.6%
 
ValueCountFrequency (%) 
91< 0.1%
 
113< 0.1%
 
121< 0.1%
 
1370.1%
 
152< 0.1%
 
ValueCountFrequency (%) 
47260.4%
 
4580.1%
 
433415.4%
 
4092014.7%
 
39390.6%
 

screenshot_num
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.873803446
Minimum0
Maximum5
Zeros1006
Zeros (%)16.0%
Memory size49.0 KiB
2020-11-15T21:28:36.438817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median5
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.87970689
Coefficient of variation (CV)0.4852354841
Kurtosis0.1254391419
Mean3.873803446
Median Absolute Deviation (MAD)0
Skewness-1.366499363
Sum24281
Variance3.533297994
MonotocityNot monotonic
2020-11-15T21:28:36.556048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
5414566.1%
 
0100616.0%
 
46119.7%
 
32393.8%
 
11392.2%
 
21282.0%
 
ValueCountFrequency (%) 
0100616.0%
 
11392.2%
 
21282.0%
 
32393.8%
 
46119.7%
 
ValueCountFrequency (%) 
5414566.1%
 
46119.7%
 
32393.8%
 
21282.0%
 
11392.2%
 

lang_num
Real number (ℝ≥0)

Distinct57
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.89023612
Minimum0
Maximum75
Zeros19
Zeros (%)0.3%
Memory size49.0 KiB
2020-11-15T21:28:36.893764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q39
95-th percentile22
Maximum75
Range75
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.202601315
Coefficient of variation (CV)1.392575976
Kurtosis10.63896235
Mean5.89023612
Median Absolute Deviation (MAD)0
Skewness2.709101859
Sum36920
Variance67.28266833
MonotocityNot monotonic
2020-11-15T21:28:37.050410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1321051.2%
 
24336.9%
 
112654.2%
 
51923.1%
 
31903.0%
 
121742.8%
 
101672.7%
 
41442.3%
 
81432.3%
 
61382.2%
 
Other values (47)121219.3%
 
ValueCountFrequency (%) 
0190.3%
 
1321051.2%
 
24336.9%
 
31903.0%
 
41442.3%
 
ValueCountFrequency (%) 
751< 0.1%
 
741< 0.1%
 
693< 0.1%
 
681< 0.1%
 
631< 0.1%
 

size_Megabytes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5644
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean196.2118044
Minimum0.56
Maximum3839.46
Zeros0
Zeros (%)0.0%
Memory size49.0 KiB
2020-11-15T21:28:37.221110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.56
5-th percentile12.4535
Q149.3225
median97.4
Q3179.7425
95-th percentile836.9905
Maximum3839.46
Range3838.9
Interquartile range (IQR)130.42

Descriptive statistics

Standard deviation336.2981051
Coefficient of variation (CV)1.7139545
Kurtosis27.92900557
Mean196.2118044
Median Absolute Deviation (MAD)58.325
Skewness4.583616811
Sum1229855.59
Variance113096.4155
MonotocityNot monotonic
2020-11-15T21:28:37.379715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
31.240.1%
 
51.53< 0.1%
 
76.253< 0.1%
 
91.643< 0.1%
 
28.323< 0.1%
 
70.533< 0.1%
 
34.513< 0.1%
 
65.883< 0.1%
 
45.283< 0.1%
 
122.293< 0.1%
 
Other values (5634)623799.5%
 
ValueCountFrequency (%) 
0.561< 0.1%
 
0.591< 0.1%
 
0.641< 0.1%
 
0.671< 0.1%
 
0.681< 0.1%
 
ValueCountFrequency (%) 
3839.461< 0.1%
 
3715.621< 0.1%
 
3681.571< 0.1%
 
3677.931< 0.1%
 
3478.041< 0.1%
 

Interactions

2020-11-15T21:28:17.168002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:17.312068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:17.456539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:17.593199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:17.736769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:17.923022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:18.068377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:18.200759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:18.387227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:18.529426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:18.678815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:18.854866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:19.006890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:19.188691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:19.546548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:19.781280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:20.059715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:20.316281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:20.522802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:20.659637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:20.813255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:20.950544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:21.096215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:21.246131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:21.405283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:21.559271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:21.705496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:21.887254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:22.127517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:22.395187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:22.602682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:22.748552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:22.921824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:23.089451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:23.244762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:23.400638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:23.597222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:23.806553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:23.990225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:24.300499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:24.505167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:24.693889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:25.029944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:25.265219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:25.625826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:25.829361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:25.971656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:26.131032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:26.274617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:26.445749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:26.608861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:26.757204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:26.913119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:27.062564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:27.267232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:27.408927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:27.563403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:27.726834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:27.937940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:28.133766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:28.277804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:28.420759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:28.563258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:28.718096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:28.930100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:29.193433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:29.373687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:29.558754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:29.800818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:30.090903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:30.279557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:30.432554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:30.596206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:30.777605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:30.941280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:31.095990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:31.279021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:31.439561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:31.616714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:31.765557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:31.917953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-15T21:28:37.527742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-15T21:28:37.733226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-15T21:28:37.932834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-15T21:28:38.145103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-11-15T21:28:38.346625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-11-15T21:28:32.365740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-15T21:28:32.712158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

idtrack_namesize_bytescurrencypricerating_count_totuser_ratingversion_numcont_ratingprime_genresup_devices_numscreenshot_numlang_numsize_Megabytes
0487119327Head Soccer121319424USD0.004815645.05.4.14+Games40411115.70
1930574573Sniper 3D Assassin: Shoot to Kill Gun Game157851648USD0.003865215.01.17.617+Games4059150.54
2387428400Infinity Blade624107810USD0.993264825.01.4.112+Games43513595.20
3887947640CSR Racing 21944321024USD0.002571005.01.11.34+Games375111854.25
4552039496The Room338273280USD0.991439085.01.0.49+Games2451322.60
5635573390Sniper Shooter: Gun Shooting Games113509376USD0.001340805.05.0.412+Games3751108.25
6448639966Pic Collage - Picture Editor & Photo Collage Maker109210624USD0.001234335.07.12.1712+Photo & Video37512104.15
7392988420Zappos: shop shoes & clothes, fast free shipping70325248USD0.001036555.03.9.04+Shopping374167.07
8519817714Credit Karma: Free Credit Scores, Reports & Alerts95494144USD0.001016795.04.11.14+Finance370191.07
91093190533PewDiePie's Tuber Simulator266766336USD0.00908515.01.9.19+Games37517254.41

Last rows

idtrack_namesize_bytescurrencypricerating_count_totuser_ratingversion_numcont_ratingprime_genresup_devices_numscreenshot_numlang_numsize_Megabytes
62581099079526脱出ゲーム 研究所からの脱出177694720USD0.0011.01.0.04+Games3851169.46
62591107529451【推理ゲーム】 YASU-第7捜査課事件ファイル-72283136USD0.0011.01.012+Games385168.93
62601115981170好きになったら負け。 完全無料!女性向けイケメン恋愛ゲーム37828608USD0.0011.01.1.09+Games3851636.08
62611116944520精度93% 絶望の心理テスト28856320USD0.0011.01.012+Lifestyle380127.52
62621135283959PokéCatcher - Cheat for Pokémon GO11693056USD0.9911.01.04+Utilities373711.15
62631141308767Tinkerblocks – code, create, play185029632USD1.9911.01.0.04+Education37512176.46
62641144076945One More Spin! - Free Vegas Casino Slots127716352USD0.0011.01.0417+Games3701121.80
62651151268606脱出ゲーム In My Heart160161792USD0.0011.01.0.04+Games3851152.74
62661182265441脱出ゲーム わたしをみつけて -おじいさんとわたしの物語-177498112USD0.0011.01.0.14+Games3841169.28
62671051656345My8to1810645504USD0.00101.02.0.14+Sports370910.15